Main Results of paper one (Hr size correlates)
1. PC analysis: How did you come up with the pcs and what are the results?
(present a table as in PAOC, with factor scores and explained variation, see nadias table)
2. Represent (justify your intuition) your selection of plots in PC1 and PC2 graph. Paoc figure.
See Reynald (ecology dept) for the interpretation of the PC proprerly. Currently large values of
PC2 indicate good forest while high values of PC1 indicate diverse well established undergrowth.
3. Model explaining variation in HR size
Main points of all the results so far condcuted in SaS
HR size plot and fragment
No sig diff in Hr size btn fragments but a tendency exist Ch>NG>MB P=0.09
No sd in Hr size btn plots but a tendency exist 1>2>3 (disturbed,less, intact) within a fragment?
P=0.68
No sd in Hr size and plotXfrgment interaction P=0.86
PC1 with plot and fragment
Fragments highly significantly differ in Pc1 CH<MB<NG p=0.0001
Plots also highly significantly differ in PC1 1<3 (more disturbed plot < intact plots)
p=0.0001
Interaction between plot and fragment in pc1 also significant (when Chawaia and mbololo are
considered 1 <3.Ngangao a special case cos it has an intermediate plot (2). p=0.0001
Pc2 with plot and fragment
Fragments highly significantly differ in Pc2 CH<NG<MB p=0.0001
Plots also highly significantly differ in PC2 1<3 (more disturbed plot < intact plots)
p=0.0001
Interaction between plot and fragment in pc2 not significant P=0.57
What are the assumptions of the analysis you intent to carry out and do you meet the
criteria? e.g. explanatory variables should not be correlated, interactions etc. How did you
go about checking all this? report thus show that you considered all those conditions and
factors.
correlation between explanatory variables PC1 PC2 INV
analysis of interaction between fragment PC1, PC2 and Inv thus
HR= frPC1 frPC2 frINV
Fragment interactions were assessed by considering the above relation as random effects and
inspecting the variation explained by the relationship. PC2 and Inv interactions with fragments
did not explain any variation in the model (HR size) but PC1 interaction with fragment did
explain some variation . This interaction is only a problem if the variation explained is
significant.
Two Complicating features for the analysis
PC1appears correlated with PC2 (person coeff -0.48 p=0.0006)
An ineraction exists btn PC1 and fragment
PC1 correlation with PC2
when data points are marked by plot (1-7), it claerly shows clumping by plot and therefore the
apparent storng negative correlation is a plot effect and not a real PC1 PC2 correlation. It is
therfore statiaticaly correct to fit them in a single model and the interpretation of the results
should be straight forward (thus effect e;g; HR size = pc 1 should just be interpreted as such. No
elements of pc2 are carried in pc 1 at least at the individual sampling points, which are our points
of interest)
PC1 interaction with fragment
PC1 fragment interaction was tested if it was significant by condcuting a likelihood ration test.
Two models were run, one with the interaction (frPC1f) HR= frPC1 frPC2 frINV and one
without the interaction thus HR= frPC2 frINV and the two log likelihood figures subtracted from
one another 27.7245-26.2237= 1.53,,,, This is less than the significant value which is grater than
3. Similar test to chi square. One df.
persimonious model
Hrsize=pc1 PC2 inv .